Though it is possible to use action-scene correlations to try improving accuracy, we show empirically it does not consistently improve results and performs worse than AMT. A possible reason is that features learned while ignoring the scene are much more representative of an action. Further, unbalanced datasets suffer from selection bias [1]}. To mitigate the effects of selection bias and encourage independent feature learning [2]}, [3]}, we use AMT and its negative branches.
